where \(\beta \)  is introduced as an “inverse temperature” and can be used to enhance or suppress probabilities corresponding to energy minima. The dynamical evolution of Eq. (REF )-(REF ) enables probabilistic sampling and inference, learning weights of a stochastic neural network or performing search or optimization in the exponential state space of the model. Such a machine evolving to the Boltzmann distribution defined by Eq. (REF ) is called a Boltzmann Machine, after the pioneering work of Hinton and colleagues [1]}, [2]}.
